The improved treatment of knee injuries critically relies on having an accurate and cost-effective detection. In recent years, deep-learning-based approaches have monopolized knee injury detection in MRI studies. The aim of this paper is to present the findings of a systematic literature review of knee (anterior cruciate ligament, meniscus, and cartilage) injury detection papers using deep learning. The systematic review was carried out following the PRISMA guidelines on several databases, including PubMed, Cochrane Library, EMBASE, and Google Scholar. Appropriate metrics were chosen to interpret the results. The prediction accuracy of the deep-learning models for the identification of knee injuries ranged from 72.5–100%. Deep learning has the potential to act at par with human-level performance in decision-making tasks related to the MRI-based diagnosis of knee injuries. The limitations of the present deep-learning approaches include data imbalance, model generalizability across different centers, verification bias, lack of related classification studies with more than two classes, and ground-truth subjectivity. There are several possible avenues of further exploration of deep learning for improving MRI-based knee injury diagnosis. Explainability and lightweightness of the deployed deep-learning systems are expected to become crucial enablers for their widespread use in clinical practice.

Purpose: This study aimed to evaluate the long‐term outcomes of medial open wedge high tibial osteotomy (MOWHTO) as a treatment option for advanced medial compartment knee osteoarthritis (OA) Kellgren–Lawrence (K‐L) III and IV.Methods: Patients with severe medial compartment arthritis, who underwent MOWHTO with locking plate between 2003 and 2015, were retrospectively reviewed. A locking plate for the osteotomy was utilized. Preoperative and postoperative patients’ evaluation was performed using the International Knee Documentation Committee Score (IKDC), the Oxford Knee Score (OKS), the Knee Injury Osteoarthritis Outcome Score (KOOS) and the Short Form‐12 Score (SF‐12). Standardized standing whole‐limb X‐rays were taken to evaluate the mechanical tibiofemoral angle (mTFA) and proximal medial tibial angle (PMTA), and the severity of OA.Results: A total of 32 patients, 35 knees (27 males, five females) of which 21 were classified as K‐L Grade III and 14 as K‐L Grade IV, and mean age 47.1 ± 9.17 years old, who were followed for 13.6 years (range 7–20 years), were included in the study. During the follow‐up period, three knees required conversion to total knee replacement (91.5% survival rate). All clinical outcome scores (KOOS, OKS, IKDC and SF‐12) showed a significant improvement compared to preoperative status (p < 0.05). Preoperative mTFA and PMTA were significantly corrected immediately after surgery and retained this improvement at the last follow‐up.Conclusion: MOWHTO with a locking plate is an effective method to treat severe medial compartments. Clinical and radiological results are satisfactory and the survival rate is 91.5%, at a mean follow‐up of 13.6 years after the procedure.Level of Evidence: Level IV.

Infection following anterior cruciate ligament (ACL) reconstruction can be one of the most debilitating complications following ACL reconstruction. Its reported incidence is around 1%. Utilization of vancomycin for presoaking the graft is considered an established method for infection prevention. The role of other agents, such as gentamycin needs further investigation. Staphylococci are the predominant causative pathogens, while particular attention should be paid to fungal infections due to their long-standing, occult process. Recent data demonstrate that hamstrings autograft may be at an elevated risk of being contaminated leading to subsequent septic arthritis. Diagnosis is set by clinical and laboratory findings and is usually confirmed by intraoperative cultures. Treatment varies, mainly depending on the intraoperative assessment. Satisfactory outcomes have been reported with both graft retaining and removal, and the decision is made upon the arthroscopic appearance of the graft and the characteristics of the infection. Of note, early management seems to lead to superior results, while persistent infection should be managed with graft removal in an attempt to protect the articular cartilage and the knee function.

Groin pain is a common symptom in athletes. The complex anatomy of the area and the various terms used to describe the etiology behind groin pain have led to a confusing nomenclature. To solve this problem, three consensus statements have been already published in the literature: the Manchester position statement in 2014, the Doha agreement in 2015 and the Italian consensus in 2016. However, when revisiting recent literature, it is evident that the use of non-anatomic terms remains common and the diagnoses sports hernia, sportsman's hernia, sportsman's groin, Gilmore's Groin, athletic pubalgia and core muscle injury are still used by many authors. Why are they still in use although rejected? Are they considered synonyms, or they are used to describe different pathology? This current concepts review article aims to clarify the confusing terminology by examining to which anatomical structures authors refer when using each term, revisit the complex anatomy of the area including the adductors, the flat and vertical abdominal muscles, the inguinal canal and the adjacent nerve branches, and propose an anatomical approach, which will provide the basis for improved communication between healthcare professionals and evidence-based treatment decisions.

Modern lifestyles require new tools for determining a person’s ability to return to daily activities after knee surgery. These quantitative instruments must feature high discrimination, be non-invasive, and be inexpensive. Machine learning is a revolutionary approach that has the potential to satisfy the aforementioned requirements and bridge the knowledge gap. The scope of this study is to summarize the results of a systematic literature review on the identification of gait-related changes and the determination of the functional recovery status of patients after knee surgery using advanced machine learning algorithms. The current systematic review was conducted using multiple databases in accordance with the PRISMA guidelines, including Scopus, PubMed, and Semantic Scholar. Six out of the 405 articles met our inclusion criteria and were directly related to the quantification of the recovery status using machine learning and gait data. The results were interpreted using appropriate metrics. The results demonstrated a recent increase in the use of sophisticated machine learning techniques that can provide robust decision-making support during personalized post-treatment interventions for knee-surgery patients.